Abstract
Context. Herschel operated as an observatory, and therefore it did not cover the whole sky, but still observed ~8% of it. The first version of an overall Herschel/PACS Point Source Catalogue (PSC) was released in 2017. The data are still unique and are very important for research using far-infrared information, especially because no new far-infrared mission is foreseen for at least the next decade. In the framework of the NEMESIS project, we revisited all the photometric observations obtained by the PACS instrument on-board the Herschel space observatory, using more advanced techniques than before, including machine learning techniques.
Aims. Our aim was to build the most complete and most accurate Herschel/PACS catalogue to date. Our primary goal was to increase the number of real sources, and decrease the number of spurious sources identified on a strongly variable background, which is due to the thermal emission of the interstellar dust, mostly located in star-forming regions. Our goal was to build a blind catalogue, meaning that source extraction is conducted without relying on prior detections at various wavelengths, allowing us to detect sources never catalogued before.
Methods. The methods for data analysis have evolved continuously since the first release of a uniform Herschel/PACS catalogue. We define a hybrid strategy that includes classical and machine learning source identification and characterisation methods that optimise faint-source detection, providing catalogues at much higher completeness levels than before. Quality assessment also involves machine learning techniques. Our source extraction methodology facilitates a systematic and impartial comparison of sensitivity levels across various Herschel fields, a task that was typically beyond the scope of individual programmes.
Results. We created a high-reliability and a rejected source catalogue for each PACS passband: 70, 100, and 160 μm. With the high-reliability catalogue, we managed to significantly increase the completeness in all bands, especially at 70 μm. At the same time, while the number of high-reliability detections decreased, the number of sources matching with existing catalogues increased, suggesting that the purity is also higher than before. The photometric accuracy of our pipeline is ~1% based on comparison with the standard star models.
Funder
PRODEX
Horizon 2020 Framework Programme